A Dozen Ways Insurers Can Leverage Big Data to Extract Business ValueThe voluminous amount of structured and unstructured, internal and external data coursing into every organization is increasing exponentially. Data today comes via varied and disparate sources that include customer interactions in channels such as call centers, Telematics devices, social media, agent conversations, smart phones, emails, faxes, police reports, day-to-day business activities, and others. Gartner predicts an 800 per cent growth in data over the next 5 years (1).

Organizations actually process only about 10 per cent to 15 per cent of available data, almost all of it in structured form. While managing this overwhelming data !ow can be challenging, insurers that can capture, store, search, aggregate, and eventually analyze the data can "nd themselves reaping very real bene"ts like increased productivity, improved competitive advantage and enhanced customer experience. This value does not necessarily come from simply managing big data, but rather, from harnessing the actionable insights from it. Insurers that can glean objective-driven business value by applying science to their data and e#ectively mine data for insights will maintain competitive advantage and stay ahead of the curve in this information age.

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About the Authors

Ajay Bhargava, Director, Analytics & Big Data, TCS

Ajay Bhargava has more than 23 years of Industry, Research, and Teaching experience in the areas relating to Databases, Enterprise Data Management, Data Warehousing, Business Intelligence, and Advanced Analytics. Over the years, he has provided business and technology oriented strategic thinking, mentoring, and customer-centric solutions to his clients.

Ajay heads up the Analytics & Big Data Center of Excellence for TCS for Insurance & Healthcare customers. He publishes articles, and is a speaker & panelist at various conferences on Enterprise Data Management, Business Intelligence, and Advanced Analytics. As an Adjunct Faculty, he has also taught at The University of Texas, Austin and College of Engineering Pune, India.

Ajay holds an M.S. in Computer Science and M.S. in Aerospace Engineering from The University of Texas at Arlington. He obtained his B.Tech in Aeronautical Engineering from Indian Institute of Technology (I.I.T.), Mumbai, 1984.

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Table of Contents

Big Data, De"ned 4

Harnessing and Harvesting Big Data 6

Value Creation from Big Data — Use Cases 8

The Opportunity 12

Getting Started 12

Big Data Challenges: At-A-Glance 14

References 15

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Big Data, De!ned

Big data provides opportunities in existing environments, but also creates new opportunities for business stakeholders that were not possible by dealing with structured content in traditional ways. Although there is certainly a lot of industry hype surrounding big data, it can be described using a combination of the following "ve characteristics:

Volume: Big data refers to the enormous – and exponentially growing – amount of data !ooding into and out of every insurance enterprise. Examples of these can be found in a variety of sources including:

There are currently more than 100 vendors providing external data services to insurers such as Insurance Services Organization (ISO), Choicepoint, and Lexus/Nexus.

Variety: Proliferating channels have led to burgeoning types of data. This data explosion has presented insurance organizations with the challenge of extracting data from disparate sources beyond the usual structured environment of data warehouses and source systems that include mobile, online, agent-generated, adjustor notes, medical reports, social media, text, audio, video, log "les, and more.

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Velocity: Insurance "rms must be able to process, access, and analyze huge volumes of information as quickly as possible in order to make timely decisions. Insurers also need to:

Veracity: The reliability and consistency of the data – its dependability and quality – is a critical issue for insurers looking to derive meaningful insights. This runs true for both big data and “small data” as well. In some cases, such as in voice to text conversions, we "nd that even not-so-perfect data quality can result in meaningful insights, especially if insurers are trying to analyze macro-level phenomenon such as sentiment analysis.

Value: Insurers that adopt business objective-driven valuable insights will come out ahead of the pack in this challenging environment. However, in order for insurers to derive true value from big data, they must enable new business models and be able to perform analytics on the data faster

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Harnessing and Harvesting Big Data

There are two fundamental aspects to big data. The "rst is harnessing, which involves collection, administration and management of big data. The second is harvesting, the artful skills and techniques required to apply science to the data, in order to derive actionable and meaningful insight from it.

Harnessing Big Data

At the most basic level, harnessing is the amassing of big data, how insurers manage big data and how they create an ecosystem that can not only create big data but sustain it as well. Years ago, harnessing data was much easier than it is today – and bene"ts of using this data were more limited as well. It’s a combination of additional sources of data like social media as well as technology that exists today to give insurers access to that data, as well as the ability to analyze it.

Gartner estimates that between 80 per cent and 90 per cent of all data produced today is unstructured. Today, insurers can tap into a treasure trove of unstructured data of all varieties: text, audio, video, adjustor notes, click streams, and log "les, for instance, and combine it with other

Insurers can no longer manage big data with traditional technologies. Instead, insurance organizations must leverage a whole new class of platforms, like the open source Hadoop ecosystem and its commercial variants o#ered by IBM, Cloudera, Hortonworks, MapR, etc., which is a distributed "le system with the Map Reduce paradigm and technologies built on top of them. The Hadoop based frameworks represent a paradigm shift in not only being able to handle di#erent kinds of data, but also provide speedy processing capabilities on huge volumes of data. Additionally, programming languages such as “R” and Python are "nding adoption in IT departments that typically are responsible for administering and managing the big data environment. While they originated in the context of handling big data at Yahoo, Google, Amazon, Facebook, etc., these programming languages can be used to handle many traditional data processing tasks as well.

One example is the use of the Hadoop platform as an ETL enabler for business agility. Today, insurers are adopting this use case, often as their "rst experience leveraging big data to meet business service level agreements (SLAs). Even though the traditional data warehousing environment still involves structured data, insurers that invest in this platform know they will be able to support unstructured data as well, now and in the future.

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Harvesting Big Data

Unstructured data cannot be consumed in its raw form. It must be processed into a consumable form before it can be both interpreted and acted upon.

Harvesting utilizes technology and algorithms that enable insurers to analyze, deliver actionable insights and derive real value from big data. Skill sets such as statistics, data mining, econometrics, business analytics, visualization techniques, and more are in high demand as they provide a solid foundation for deriving useful insights from the data. Universities have started trying to ll the supply demand gap by o ering various graduate programs in business analytics to prepare for the next generational skills needed to mine actionable insights.

While the ability to successfully harness and harvest data is critical to a big data strategy, the harvesting aspect is where insurers derive the true value from their data, with the help of analytics.

De ning use cases and hypotheses becomes crucial when following a focused “top-down” approach to creating actionable insights (see gure above).

Business RequirementsIncludes Business Objectives,

Expected Bene ts, Use Cases and KPIs

Strategic Objectives

Actions

Data & Process RequirementsIncludes Conceptual and Dimensional Data Model,

Subject Areas and Business Processes needed to ful ll Bl and Analysis

Bl RequirementsIncludes reports/metrics/analysis needed

to ful ll Business Requirements

PAST ORIENTED

Analytics RequirementsIncludes factor analysis, trending, statistical and

predictive models to ful ll Business Requirements.

FUTURE ORIENTED

Dictate

Insights lead to

To achieve

Map to

Iterate

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Although this is a focused approach, many times insurers need to do some initial work in order to perform data exploratory analysis to even come up with the use cases utilizing big data in the rst place. This initial “bottom up” approach becomes a prerequisite for determining and prioritizing use cases for which big data Proof of Concepts (PoCs) should be pursued.

Real value is derived when actionable insights can make a positive di erence in achieving the strategic objectives. Some of the other comparisons between harnessing and harvesting are shown in the

gure below.

Harvesting and harnessing activities are complementary to one another, two sides of the big data coin. Big data platforms do not replace existing traditional data management and analytics platforms. Instead, they merely complement, add, mature, and improve upon existing environments and capabilities.

Value Creation from Big Data — Use Cases

According to McKinsey & Co. (1), big data creates value in ve ways:

First, managing big data can increase transparency, making data more easily accessible to relevant stakeholders.

Second, as they create and store more transactional data in digital form, organizations can collect accurate, detailed performance data in real-time or near real-time, enabling experimentation to identify needs and improve performance.

Third, big data gives organizations the means to improve customer segmentation and then better develop and tailor products, services, and promotions to each speci c segment.

Fourth, a big data strategy can include sophisticated analytics to provide actionable insights that minimize risks and improve decision-making.

And nally, big data can be indispensible for organizations looking to create new business models and improve products and services.

Leading-edge insurers are starting to (or are planning to) exploit big data in at least 12 di erent ways, each of which adds value to the organization in one or more of the ways described above (see chart on page 11).

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1 Making performance improvements in existing data warehouse environments. IT has begun to adopt Hadoop-based architecture to speed up ETL in a data warehouse environment to meet reporting business SLAs.

2 Detecting fraud. detection by looking at structured data in their claims and policy data warehouses and combining it with textual data in adjustor notes, police reports, and social media. Special Investigative Units (SIUs) are very interested in identifying suspicious claims or knowing which claims have subrogation or litigation potential. Text Analytics and NLP capabilities are enabling innovative solutions in detecting claims fraud, in addition to automated business rules, predictive analytics, social media analytics, and link analysis techniques.

3 Combining customer channels. Combining direct customer connections (email, call center, agent, portal, faxes, adjustor reports, etc.) with indirect customer connects such as social media, blogs, log "les etc. provides a more holistic, 3600 view of each customer. This helps to create a personalized, uni"ed communication response, enabling CMOs to achieve better brand value and gain competitive advantages while directly impacting the bottom line by reducing communication waste. Also, minimizing CapEx by using cloud technologies, along with mobile visualization techniques, enables executives to make decisions quickly and cost-e#ectively.

4 Optimizing call center workload. Analyzing telecom data from the switches (call detail records), combining it with claims, helps in understanding what activity was performed by whom, how

5 Using telematics data to derive prescriptive and predictive value. CIOs are currently investigating how analytics can suggest better driving behavior by sensing telematics data and responding in near-real time. In addition to prescriptive analytics i.e. the devices alerting on risky behavior such as speeding or seat belt non-usage, they are also interested in predicting driving behavior risks, road delays, etc.

6 Leveraging cross-sell and up-sell potential. Similarly, by analyzing text and speech in a near-real time environment, organizations are presented with new opportunities to convert the call center from a cost center to an investment center by providing cross sell and up-sell capabilities.

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7 Using NLP, text analytics to improve sentiment analysis. Using Natural Language Processing (NLP) and text analytics for social media as well as speech analytics for call center conversations, insurers are able to improve their sentiment analysis to better meet customer service improvement objectives.

8 Utilizing social media to introduce new products and services. Insurance CMOs are utilizing social media to introduce new products and services and target customers in speci"c regions in a

innovatively changing the business model. Insurers can experiment with di#erent segments, and then upgrade their strategies to a national level.

9 Closing the loop between pricing risk and claims. Underwriters are studying loss and fraud propensity of existing claimants in order to better price risk for new prospects, especially in the property casualty insurance business. This helps in minimizing risks and to a large extent, price the risk appropriately.

10 Leveraging external data for more accurate pricing.weather data can lead to more appropriate pricing on property casualty insurance based on how and where insureds actually drive their cars.

11 Enhancing search capabilities. Many reinsurers and insurance agents are using big data to discover innovative ways to search their intranet documents for providing fast search capabilities in unstructured PDFs and Word documents that were not possible previously. These are being used by their "nancial divisions, as well as in call center scenarios to provide real-time recommendations.

12 Creating comprehensive customer satisfaction surveys. Most insurance organizations perform customer surveys using a very small customer sample size. Big data enables the insurers to survey their entire customer base, processing the survey results in a fast and cost-e#ective way to paint a truer picture from their customer service responses.

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Use Cases Increase Transparency

Improve performance,

near-real time analysis

Improve customer

segmentation

Provide actionable

insights

Create new business models

to improve products, services

1 ETL Performance Improvements

2 Detecting Fraud

3 360° Customer View

4 Call Center Optimization

5 Telematics

6 Cross-Sell and Up-Sell

7 Sentiment Analysis

8 Social media for new products and services

9 Closing loop between pricing risk and claims

10 External data for pricing

11 Search on unstructured documents

12 Better customer surveys

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The Opportunity

According to June 2012 research from analyst "rm Novarica (2), in general, big data is not yet a big priority in most insurance organizations. In general, insurers are hampered by severely fragmented

to support a big data strategy. Most carriers are still maturing and expanding their use of traditional data analytics and predictive models to improve processes, reduce losses and generally improve their book of business. Insurers that aren’t exploring and embracing big data and developing a big data strategy will "nd that not only are they losing their competitive advantage and potential to achieve

into their organizations to achieve their desired business objectives. While a vast majority of insurers are using analytics for actuarial (95%) and pricing (83%), fewer than half of insurers are using analytics to improve operational areas like sales, marketing or optimized work assignment for underwriters or claims adjusters.

While relatively few insurers are fully immersed in a comprehensive big data strategy and reaping its bene"ts, the good news is that most insurers are planning their big data approach. Even fewer insurers capture, persist, and analyze big data within their computing environment today, but those that do typically leverage traditional computing, storage, database and analytics, in addition to newer platforms such as the Hadoop eco-system. In fact, larger property casualty insurers universally plan to embrace big data and analytics across all "nancial and risk management areas (as well as most operational areas). Only about half of smaller insurers are planning the same actions.

Getting Started

Big data solutions encompass a new generation of software and architectures designed to economically extract value from enormous volumes and variety of structured and unstructured data by enabling rapid data capture, discovery, and/or analysis. According to Novarica, the insurers that will be best positioned to pro"t from the potential value of big data will be those who have created a culture where business leaders trust analytics and act on the insights provided. All insurers should take steps to create that culture today if it doesn’t already exist in their companies.

The key is to start small with a Proof of Concept (PoC). Here is an example of how insurers can leverage a big data platform and some key considerations to keep in mind when embarking on a PoC. In this

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example, IT is interested in using a big data environment to speed up long-running ETL processes in a traditional data warehouse environment using structured data, which is leading the organization to miss meeting reporting SLAs for business.

It is important for insurers to develop a good business use case for meeting the strategic objectives of that line of business. In addition, solid backing from a CxO/VP level executive is absolutely required not only for funding, but to evangelize and communicate the objectives and need to the larger organization, including partners and vendors. Although the scope and investment in terms of people (~ 5 FTEs), tools (e.g. open source Hadoop eco-system), technologies and infrastructure (e.g. commodity hardware or cloud) might be small, the architecture should keep the long term view in mind. For the right harnessing and harvesting, good collaboration between IT and business is imperative to iteratively experiment and drive actionable insights. Insurers can then use this incremental success is to get increased funding for next phases and/or use cases.

As insurers identify and understand the scenarios for applying big data within their businesses, they will need to tweak their existing processes to be able to ingest the data “variety,” having good “veracity,” increasing “volume” or growing need of real-time “velocity,” to derive objective-driven actionable “value.” Insurance organizations that are able to develop an analytics-driven culture and that learn how to harness the power of big data and harvest the valuable information and insight big data provides can create competitive advantage and positively impact their brand and their top and bottom line.

PoC Objective: Increase Business Agility

Big Business Use Case/Hypothesis Speed up ETL so IT can meet reporting SLAs for business

Small Investment < 120K$

Big Executive Support Cx 0 level support

Small Scope/Design/Implementation Few longest-running ETL scripts

Big Architecture (Data Platform) Hadoop eco-system — scalable for growth

Small Team 5 FTE

Big Collaboration — IT & Business Needed for Harnessing & Harvesting respectively

Small Duration < 12 calendar weeks

Big Business Value Business is able to take timely decisions/actions

Small Incremental Success Provide funding for next phase

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Big Data Challenges: At-A-GlanceInsurers are faced with a number of factors that combine to make big data a big challenge:

The explosion of data and proliferation

An increasingly competitive landscape,

The "nancial tsunami of the past several years, as well as the resulting increasingly demanding regulatory requirements in both North America

An unusually high number of catastrophic losses caused by brush "res, hurricanes, earthquakes,

The constant stream of innovations that have made it essential for insurers to deal with big data at the same time they are managing the challenges of their existing small data environments.

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References(1) McKinsey Global Institute report, “Big Data: the Next Frontier for Innovation, Competition and Productivity,” May 2011.

(2) Novarica, Insurance Technology Research Council, Analytics and Big Data at Insurers: Current State and Expectations, June 2012.

Copyright © 2013 Tata Consultancy Services Limited

Copyright © 2013 Tata Consultancy Services Limited